What does the term 'epoch' typically refer to in machine learning?

The term 'epoch' in machine learning refers to one complete pass of the entire training dataset through the learning algorithm. Each epoch involves training the model on each data point in the dataset, updating the model parameters (like weights) based on the learning algorithm's optimization process. This is important because multiple epochs are often necessary for the model to learn effectively from the data, as it allows the model to refine its weights and improve its accuracy with each complete sweep of the dataset.

In contrast, the other choices confound the term 'epoch' with different concepts within the training process. The number of features used pertains to the dimensions or inputs of the model, computational power relates to hardware capabilities rather than the training process specifically, and batch size refers to the number of training samples utilized in one iteration of the gradient descent algorithm, which can affect the training dynamics within an epoch. Understanding the specific meaning of an epoch allows for better insights into training durations and model performance.